Sharia Bank of Indonesia Stock Price Prediction using Long Short-Term Memory
Journal Title: Journal of Economics, Finance and Management Studies - Year 2024, Vol 7, Issue 07
Abstract
This study explored the applicability of Long Short-Term Memory (LSTM) networks for predicting the closing prices of Sharia Bank of Indonesia stock. Utilizing historical data and a rigorous hyperparameter tuning process, the LSTM model demonstrated exceptional accuracy in forecasting closing prices, achieving a Mean Absolute Percentage Error (MAPE) of 2.46%. This significantly surpasses the benchmark for "very good" forecasting performance. The findings underscore the potential of LSTM networks for accurate stock price prediction and provide a foundation for future research to incorporate additional financial indicators and explore more complex model architectures.
Authors and Affiliations
Fahmi Poernamawatie , I Nyoman Susipta , Dwi Winarno,
Keywords
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- EP ID EP741444
- DOI 10.47191/jefms/v7-i7-94
- Views 40
- Downloads 0
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